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AI observability

Definition

Logging and monitoring for AI systems: what went in, what came out, which tools were called, what it cost, and how often a human overrode it. Without it, a misbehaving automation is undiagnosable.

The override rate is the most useful single metric anyone tracks here. If humans are correcting 30% of output, the system is not saving what you think it is; if they are correcting 2%, the review gate has probably become a rubber stamp.

Ask any vendor what they log and who can see it. Automations running inside a supplier's account with prompts you cannot read are a black box you rent, and you will not be able to debug or move them.

More in AI

Agentic workflow

A process where an AI system is given a goal rather than a script, and chooses its own sequence of tool calls to reach it. Distinguished from automation by being non-deterministic: the same input can produce a different route each run.

AI agent

An AI agent is a system that pursues a goal across multiple steps using tools — reading data, calling APIs, updating records — rather than producing a single response to a single prompt.

AI automation ROI

The payback calculation for an automation: hours recovered multiplied by fully loaded hourly cost, against build plus running cost. Frequency dominates the result — the same build against a process running twice a month rarely pays back at all.

AI readiness

Whether a business has the process documentation, data quality and decision ownership for AI to add value. Readiness is mostly organisational: the constraint is almost never model capability.

Automation debt

The accumulated cost of automations nobody owns: undocumented flows, forgotten triggers, and scripts that break silently when an upstream field is renamed. The AI-era version of technical debt.

Brand voice model

A brand voice model is a language model customised on a company's own copy, offers and guidelines so its output matches that brand's register, vocabulary and positioning without heavy editing.

All 87 terms
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